抑郁面部模式是否跨文化和情境迁移？来自德国随机对照试验和E-DAIC的证据
Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence from a German RCT and E-DAIC
- FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
- LMU München（慕尼黑大学）
- Helmholtz Zentrum München（亥姆霍兹慕尼黑中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过德国RCT与E-DAIC的双向迁移实验，发现功能情境对齐决定跨语料库泛化，被动观察情境下抑郁面部模型迁移最佳，二元分类优于连续回归。
AI中文摘要：
基于面部动态的抑郁症自动评估有望实现可扩展的心理健康监测，但所学生物标志物的跨语料库泛化仍是一个开放挑战。我们开展了一项系统的双向迁移研究，将EmpkinS-EKSpression随机对照试验（RCT；N=256，SCID-5-CV诊断）与扩展困扰分析访谈语料库（E-DAIC；N=275，半结构化临床访谈）配对，从面部动作单元、头部姿态和注视预测抑郁严重程度和二元诊断状态。跨语料库二元分类比连续PHQ-8严重程度回归更稳健，前向迁移达到AUC=0.70。回归迁移受功能情境对齐支配：被动观察阶段产生最具迁移性的模型，而主动情绪调节阶段在语料库内引发更强信号。这些发现确立功能情境对齐为跨语料库泛化的主要决定因素，被动诱发情境在语料库内敏感性与跨语料库稳健性之间提供最佳权衡。
英文摘要：
Automated assessment of depression from facial dynamics holds promise for scalable mental health monitoring, yet cross-corpus generalization of learned biomarkers remains an open challenge. We present a systematic bidirectional transfer study pairing the EmpkinS-EKSpression randomized controlled trial (RCT; N = 256, SCID-5-CV diagnoses) with the Extended Distress Analysis Interview Corpus (E-DAIC; N = 275, semi-structured clinical interviews), predicting depression severity and binary diagnostic status from facial action units, head pose, and gaze. Cross-corpus binary classification proves more robust than continuous PHQ-8 severity regression, with forward transfer achieving AUC = 0.70. Regression transfer is governed by functional context alignment: passive observation phases yield the most transferable models, while active emotion regulation phases elicit stronger within-corpus signals. These findings establish functional context alignment as the primary determinant of cross-corpus generalization, with passive elicitation contexts offering the best trade-off between within-corpus sensitivity and cross-corpus robustness.
